As farming practices have changed, so have the methods for treatment of plants in the field. The increased uptake of conservation farming practices—including minimum-till and no-till practices—has seen an increase in herbicide (and other types of pesticides) usage for the control of weeds, and this increase in usage is causing selective breeding of tolerance characteristics (also known as “resistance” in the farming industry) to the herbicide in successive generations. Currently, the standard technique for breaking this tolerance is to use a herbicide with a different killing action (which is often more expensive than the herbicides already being used) and/or mechanical cultivation. Typically, the herbicide is applied with a boom sprayer that either sprays herbicide in a broadcast manner on both the weeds and the crops or is focused such that the herbicide is only applied in the area between the crop rows. In either method, the herbicide is sprayed continually across the field.
One alternative cost efficient way to apply the different, more costly “specific action” or “selective” herbicides is through automatic spot spraying of the weeds. However, current commercial spot spraying technologies are only capable of recognizing the presence of vegetation (distinguishing plants from background such as soil or stubble)—the technologies do not have the ability to identify the specific vegetation and thus can't distinguish weed plants from crop plants. Further, additional experimental technologies have been developed for controlled conditions and consequently are not suitable to achieve a workable commercial solution.
In addition, there is the possibility of applying a variety of other agricultural chemicals such as pesticides, barrier treatments, or even spot treatment of fertilizers, using image recognition of the features to be sought to identify a point or area of treatment in an agricultural field.
Machine vision technologies are used in a variety of different systems and methods, including, for example, driverless vehicles, automated object selection, and various other vision-aided robotic or automated systems. Several methods have been used for segmenting objects in the various machine vision technologies. In these various systems, varied lighting conditions can impact the effectiveness of the known machine vision technology and segmentation processes. One method for addressing the varied lighting conditions is a shadow detection process. However, known shadow detection processes have limitations, including incorrect segmentation caused by variation in the color of the light source and are not particularly applicable to treatment of an agricultural crop in a field without adaptation. For example, the color variation in the light source degrades the segmentation quality so that either plant material captured in the image is missed or portions of non-plant material are incorrectly categorized as plant material.
Another problem with the known process of real-time shadow detection and correction is processing time. That is, the known methods do not have the processing speed necessary for real-time systems usable for the real time processing of shadows in a system for detection and treatment in an agricultural field. Processing time is limited in real-time systems such as automated spot spraying or vision guidance systems where the frame rate may need to be, for example, 30 frames per second (“FPS”) or faster. In such exemplary known systems, a frame rate of 30 FPS or faster leaves less than 33 milliseconds (“ms”) to compensate for shadows and daylight, identify the presence of the target object (such as a weed or crop row, for example), and determine the action required. The known methods cannot operate at that speed.
There is a need in the art for improved systems and methods for shadow detection in real-time feature identification, particularly real time detection of plants in an agricultural treatment system.